Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy

Authors

• Kohei Yamashita
• Evan Kwiatkowski
• Matheus Sewastjanow-Silva
• Jane E Rogers
• Katsuhiro Yoshimura
• Melissa Pool Pizzi
• Qiong Gan
• Jenny J Li
• Rebecca E Waters
• Mariela Blum Murphy
• Masaaki Iwatsuki
• Wayne L Hofstetter
• Aileen Chen
• Ying Yuan
• Jaffer A Ajani

Language

English

Publication Date

8-1-2026

Journal

Annals of Surgical Oncology

DOI

10.1245/s10434-026-19557-6

PMID

42002713

PMCID

PMC13337599

PubMedCentral® Posted Date

4-19-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Background: Accurate prediction of pathological complete response (pCR) after preoperative chemoradiation therapy, followed by surgery (trimodality therapy) in esophageal adenocarcinoma (EAC) and gastroesophageal junction adenocarcinoma (GEJAC) may improve clinical decision-making and patient counseling before esophagectomy. This study aimed to develop predictive models for pCR after trimodality using machine learning (ML) approaches.

Patients and methods: A total of 569 patients with EAC and GEJAC who received trimodality therapy at MD Anderson Cancer Center between 2002 and 2022 were included. Clinicopathological characteristics and survival benefit of patients who achieved a pCR were reviewed via descriptive and survival analyses. Subsequently, ML models based on clinical variables were employed to predict pCR, including BART, random forest, and XGBoost, logistic regression, and LASSO.

Results: pCR was achieved in 132 patients (23.2%). Poorly differentiated tumors, tumors with signet ring cell component, higher T stage, higher clinical stage, residual tumor on biopsy after chemoradiation, and higher SUVmax on positron emission tomography-contract tomography (PET-CT) after chemoradiation were significantly associated with non-pCR. pCR patients had significantly longer overall survival (OS) and relapse free survival (RFS) compared with non-pCR patients (median OS, 10.40 versus 4.42 years, log-rank p = 0.0041; median RFS, 10.40 versus 2.35 years, log-rank p < 0.0001). The random forest model showed the highest predictive ability for pCR with an AUC value of 0.702 among the employed models.

Conclusions: This first exploratory study supports the validity and potential utility of ML-based models for predicting pCR after trimodality therapy in EAC and GEJAC. Further validation is warranted before clinical application.

Keywords

Humans, Pathologic Complete Response, Esophageal Neoplasms, Female, Esophagogastric Junction, Adenocarcinoma, Male, Predictive Learning Models, Combined Modality Therapy, Middle Aged, Survival Rate, Machine Learning, Prognosis, Chemoradiotherapy, Follow-Up Studies, Random Forest, Esophagectomy, Aged, Retrospective Studies, Boosting Machine Learning Algorithms

Published Open-Access

yes

Recommended Citation

Yamashita, Kohei; Kwiatkowski, Evan; Sewastjanow-Silva, Matheus; et al., "Clinical Machine Learning Model for Predicting Pathological Complete Response in Patients with Esophageal and Gastroesophageal Junction Adenocarcinoma After Trimodality Therapy" (2026). Faculty, Staff and Student Publications. 1459.
https://digitalcommons.library.tmc.edu/uthsph_docs/1459